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BackgroundIt is important but challenging to understand the interactions of multiple chronic conditions (MCC) and how they develop over time in patients and populations. Clinical data on MCC can now be represented using graphical models to study their interaction and identify the path toward the development of MCC. However, the current graphical models representing MCC are often complex and difficult to analyze. Therefore, it is necessary to develop improved methods for generating these models. ObjectiveThis study aimed to summarize the complex graphical models of MCC interactions to improve comprehension and aid analysis. MethodsWe examined the emergence of 5 chronic medical conditions (ie, traumatic brain injury [TBI], posttraumatic stress disorder [PTSD], depression [Depr], substance abuse [SuAb], and back pain [BaPa]) over 5 years among 257,633 veteran patients. We developed 3 algorithms that utilize the second eigenvalue of the graph Laplacian to summarize the complex graphical models of MCC by removing less significant edges. The first algorithm learns a sparse probabilistic graphical model of MCC interactions directly from the data. The second algorithm summarizes an existing probabilistic graphical model of MCC interactions when a supporting data set is available. The third algorithm, which is a variation of the second algorithm, summarizes the existing graphical model of MCC interactions with no supporting data. Finally, we examined the coappearance of the 100 most common terms in the literature of MCC to validate the performance of the proposed model. ResultsThe proposed summarization algorithms demonstrate considerable performance in extracting major connections among MCC without reducing the predictive accuracy of the resulting graphical models. For the model learned directly from the data, the area under the curve (AUC) performance for predicting TBI, PTSD, BaPa, SuAb, and Depr, respectively, during the next 4 years is as follows—year 2: 79.91%, 84.04%, 78.83%, 82.50%, and 81.47%; year 3: 76.23%, 80.61%, 73.51%, 79.84%, and 77.13%; year 4: 72.38%, 78.22%, 72.96%, 77.92%, and 72.65%; and year 5: 69.51%, 76.15%, 73.04%, 76.72%, and 69.99%, respectively. This demonstrates an overall 12.07% increase in the cumulative sum of AUC in comparison with the classic multilevel temporal Bayesian network. ConclusionsUsing graph summarization can improve the interpretability and the predictive power of the complex graphical models of MCC.

ObjectiveDescribe dementia cases identified through International Classification of Diseases (ICD) coding in the Long-term Impact of Military-relevant Brain Injury Consortium - Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC) multicenter prospective longitudinal study (PLS) of mild traumatic brain injury (mTBI).DesignDescriptive case series using cross-sectional data.MethodsVeterans Affairs (VA) health system data including ICD codes were obtained for 1563 PLS participants through the VA Informatics and Computing Infrastructure (VINCI). Demographic, injury, and clinical characteristics of Dementia positive and negative cases are described.ResultsFive cases of dementia were identified, all under 65 years old. The dementia cases all had a history of blast-related mTBI and all had self-reported functional problems and four had PTSD symptomatology at the clinical disorder range. Cognitive testing revealed some deficits especially in the visual memory and verbal learning and memory domains, and that two of the cases might be false positives.ConclusionsICD codes for early dementia in the VA system have specificity concerns, but could be indicative of cognitive performance and self-reported cognitive function. Further research is needed to better determine links to blast exposure, blast-related mTBI, and PTSD to early dementia in the military population.

It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably referred to as subtypes, phenotypes, and/or clinical profiles. As part of the Brain Trauma Blueprint TBI State of the Science, we review the current literature on TBI phenotyping with an emphasis on unsupervised methodological approaches, and describe five phenotypes that appear similar across reports. However, we also find the literature contains divergent analysis strategies, inclusion criteria, findings, and use of terms. Further, whereas some studies delineate phenotypes within a specific severity of TBI, others derive phenotypes across the full spectrum of severity. Together, these facts confound direct synthesis of the findings. To overcome this, we introduce PhenoBench, a freely available code repository for the standardization and evaluation of raw phenotyping data. With this review and toolset, we provide a pathway toward robust, data-driven phenotypes that can capture the heterogeneity of TBI, enabling reproducible insights and targeted care.

Although many patients diagnosed with traumatic brain injury (TBI), particularly mild TBI, recover from their symptoms within a few weeks, a small but meaningful subset experience symptoms that persist for months or years after injury and significantly impact quality of life for the person and their family. Factors associated with an increased likelihood of negative TBI outcomes include not only characteristics of the injury and injury mechanism, but also the person's age, pre-injury status, comorbid conditions, environment, and propensity for resilience. In this article, as part of the Brain Trauma Blueprint: TBI State of the Science framework, we examine the epidemiology of long-term outcomes of TBI, including incidence, prevalence, and risk factors. We identify the need for increased longitudinal, global, standardized, and validated assessments on incidence, recovery, and treatments, as well as standardized assessments of the influence of genetics, race, ethnicity, sex, and environment on TBI outcomes. By identifying how epidemiological factors contribute to TBI outcomes in different groups of persons and potentially impact differential disease progression, we can guide investigators and clinicians toward more-precise patient diagnosis, along with tailored management, and improve clinical trial designs, data evaluation, and patient selection criteria.

Traumatic brain injury (TBI) is common among military personnel and the civilian population and is often followed by a heterogeneous array of clinical, cognitive, behavioral, mood, and neuroimaging changes. Unlike many neurological disorders that have a characteristic abnormal central neurologic area(s) of abnormality pathognomonic to the disorder, a sufficient head impact may cause focal, multifocal, diffuse or combination of injury to the brain. This inconsistent presentation makes it difficult to establish or validate biological and imaging markers that could help improve diagnostic and prognostic accuracy in this patient population. The purpose of this manuscript is to describe both the challenges and opportunities when conducting military-relevant TBI research and introduce the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Military Brain Injury working group. ENIGMA is a worldwide consortium focused on improving replicability and analytical power through data sharing and collaboration. In this paper, we discuss challenges affecting efforts to aggregate data in this patient group. In addition, we highlight how "big data" approaches might be used to understand better the role that each of these variables might play in the imaging and functional phenotypes of TBI in Service member and Veteran populations, and how data may be used to examine important military specific issues such as return to duty, the late effects of combat-related injury, and alteration of the natural aging processes.

Mild traumatic brain injury in the Veteran population is frequently comorbid with pain, post-traumatic stress disorder, and/or depression. However, not everyone exposed to mild traumatic brain injury experiences these comorbidities and it is unclear what factors contribute to this variability. The objective of this study was to identify comorbidity phenotypes among Post-9/11 deployed Veterans with no or mild traumatic brain injury and examine the association of comorbidity phenotypes with adverse outcomes. We found that Veterans with mild traumatic brain injury (n = 93,003) and no brain injury (n = 434,378) were mean age of 32.0 (SD 9.21) on entering Department of Veterans Health Administration care, were predominantly Caucasian non-Hispanic (64.69%), and served in the Army (61.31%). Latent class analysis revealed five phenotypes in each subcohort; Moderately Healthy and Mental Health phenotypes were common to both. The Healthy phenotype was found only in no brain injury. Unique phenotypes in mild traumatic brain injury included Moderately Healthy+Decline, Polytrauma, and Polytrauma+Improvement. There was substantial variation in adverse outcomes. The Polytrauma+Improvement phenotype had the lowest likelihood of adverse outcomes. There were no differences between Moderately Healthy+Decline and Polytrauma phenotypes. Phenotypes of comorbidity vary significantly by traumatic brain injury status including divergence in phenotypes (and outcomes) over time in the mild traumatic brain injury subcohort. Understanding risk factors for the divergence between Polytrauma vs. Polytrauma+Improvement and Moderately Healthy vs. Moderately Healthy+Decline, will improve our ability to proactively mitigate risk, better understand the early patterns of comorbidity that are associated with neurodegenerative sequelae following mild traumatic brain injury, and plan more patient-centered care.

Objective: This study investigated influence of biological sex on postconcussive symptoms (PCS) following concussion using the Federal Interagency Traumatic Brain Injury Research (FITBIR) database. Method: All studies with publicly released data as of 4/7/21 that included both males and females, enough information to determine severity of injury consistent with concussion, a measure of PCS, and objective measures of neurocognitive functioning were used. This resulted in 6 studies with a total of 9890 participants (3206 females, 6684 males); 815 participants completed the Neurobehavioral Symptom Inventory (NSI), 471 completed the Rivermead Post-Concussion Symptoms Questionnaire (RPSQ), and 8604 completed the Sport Concussion Assessment Tool–3rd Edition (SCAT 3). Questionnaires were harmonized and the following symptom composite scores were computed: total score, somatic, cognitive, and affective. Data were analyzed using linear mixed-effects models. Results: Females endorsed higher total symptoms relative to males and that military personnel endorsed higher symptoms relative to civilians. Additionally, there was a small but significant interaction effect, such that female military personnel endorsed even higher symptoms than would be predicted by the main effects. Similar patterns were observed for somatic, cognitive, and affective symptom domains. Conclusions: Further understanding sex differences in PCS reporting is key to informing the most appropriate treatment options. Future work will need to examine whether sex differences in symptom reporting is due to sex differences in endorsement styles or genuine differences in symptom presentation, as well as the relationship between study population (e.g., military, civilian, sport) and sex on objective cognitive functioning and other functional outcomes. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy is associated with significant mortality risk. There is limited research examining how traumatic brain injury (TBI) timing affects mortality in relation to the onset of epilepsy. We aimed to assess the temporal relationship between epilepsy and TBI regarding mortality in a cohort of post‐9/11 veterans. Methods: This retrospective cohort study included veterans who received health care in the Defense Health Agency and the Veterans Health Administration between 2000 and 2019. For those diagnosed with epilepsy, the index date was the date of first antiseizure medication or first seizure; we simulated the index date for those without epilepsy. We created the study groups by the index date and first documented TBI: (1) controls (no TBI, no epilepsy), (2) TBI only, (3) epilepsy only, (4) TBI before epilepsy, (5) TBI within 6 months after epilepsy, and (6) TBI >6 months after epilepsy. Kaplan–Meier estimates of all‐cause mortality were calculated, and log‐rank tests were used to compare unadjusted cumulative mortality rates among groups compared to controls. Cox proportional hazard models were used to compute hazard ratios (HRs) with 95% confidence intervals (CIs). Results: Among 938 890 veterans, 27 436 (2.92%) met epilepsy criteria, and 264 890 (28.22%) had a TBI diagnosis. Mortality was higher for veterans with epilepsy than controls (6.26% vs. 1.12%; p <.01). Veterans with TBI diagnosed ≤6 months after epilepsy had the highest mortality hazard (HR = 5.02, 95% CI = 4.21–5.99) compared to controls, followed by those with TBI before epilepsy (HR = 4.25, 95% CI = 3.89–4.58), epilepsy only (HR = 4.00, 95% CI = 3.67–4.36), and TBI >6 months after epilepsy (HR = 2.49, 95% CI = 2.17–2.85). These differences were significant across groups. Significance: TBI timing relative to epilepsy affects time to mortality; TBI within 6 months after epilepsy or before epilepsy diagnosis was associated with earlier time to death compared to those with epilepsy only or TBI >6 months after epilepsy. [ABSTRACT FROM AUTHOR]

Research indicates that being married is associated with reduced risk of suicide and self-directed violence (SDV) relative to being divorced. Simultaneously, difficulties within relationships predict poorer health outcomes. However, research on relationship status rarely examines relationship functioning, obfuscating the joint contribution of these variables for SDV risk. Veterans (N = 1,049) completed a survey that included assessment of relationship status, relationship functioning, and SDV history. Logistic regression models tested how (a) relationship status, (b) relationship dysfunction, and (c) being divorced compared to being in a low- or high-dysfunction relationship were associated with SDV, controlling for several intrapersonal risk factors. Veterans in a relationship did not differ in SDV history compared to divorced/separated veterans. However, more dysfunction within relationships was associated with greater odds of a history of SDV and suicidal cognitions. Finally, SDV histories were more likely among veterans endorsing high-dysfunction relationships compared with (a) low-dysfunction relationships and (b) divorced veterans. It may be insufficient to only consider relationship status when evaluating interpersonal risk factors for SDV. A single item assessing relationship dysfunction was associated with enacted SDV and suicidal cognitions over and above intrapersonal risk factors. Integrating such single-item measures into clinical practice could improve identification and subsequent tailored intervention for veterans at greater risk for SDV. Relationship dysfunction was related to self-directed violence (SDV) history independent of other risk factors. Being in a relationship alone was not related to SDV history relative to being divorced. A single item assessing relationship dysfunction was related to SDV history. [ABSTRACT FROM AUTHOR]

Objective: Post-9/11-era veterans with traumatic brain injury (TBI) have greater health-related complexity than veterans overall, and may require coordinated care from TBI specialists such as those within the Department of Veterans Affairs (VA) healthcare system. With passage of the Choice and MISSION Acts, more veterans are using VA-purchased care delivered by community providers who may lack TBI training. We explored prevalence and correlates of VA-purchased care use among post-9/11 veterans with TBI. Setting: Nationwide VA-purchased care from 2016 through 2019. Participants: Post-9/11-era veterans with clinician-confirmed TBI based on VA's Comprehensive TBI Evaluation (N = 65 144). Design: This was a retrospective, observational study. Main Measures: Proportions of veterans who used VA-purchased care and both VA-purchased and VA-delivered outpatient care, overall and by study year. We employed multivariable logistic regression to assess associations between veterans' sociodemographic, military history, and clinical characteristics and their likelihood of using VA-purchased care from 2016 through 2019. Results: Overall, 51% of veterans with TBI used VA-purchased care during the study period. Nearly all who used VA-purchased care (99%) also used VA-delivered outpatient care. Veterans' sociodemographic, military, and clinical characteristics were associated with their likelihood of using VA-purchased care. Notably, in adjusted analyses, veterans with moderate/severe TBI (vs mild), those with higher health risk scores, and those diagnosed with posttraumatic stress disorder, depression, anxiety, substance use disorders, or pain-related conditions had increased odds of using VA-purchased care. Additionally, those flagged as high risk for suicide also had higher odds of VA-purchased care use. Conclusions: Veterans with TBI with greater health-related complexity were more likely to use VA-purchased care than their less complex counterparts. The risks of potential care fragmentation across providers versus the benefits of increased access to care are unknown. Research is needed to examine health and functional outcomes among these veterans. [ABSTRACT FROM AUTHOR]

Objective: To examine whether post-9/11 veterans who screened positive for mild traumatic brain injury (mTBI) but did not complete a Comprehensive TBI Evaluation (CTBIE) were at higher risk of subsequent adverse events compared with veterans who screened positive and completed a CTBIE. Upon CTBIE completion, information assessed by a trained TBI clinician indicates whether there is mTBI history (mTBI+) or not (mTBI−). Setting: Veterans Health Administration (VHA) outpatient services. Participants: A total of 52 700 post-9/11 veterans who screened positive for TBI were included. The follow-up review period was between fiscal years 2008 and 2019. The 3 groups studied based on CTBIE completion and mTBI status were: (1) mTBI+ (48.6%), (2) mTBI− (17.8%), and (3) no CTBIE (33.7%). Design: This was a retrospective cohort study. Log binomial and Poisson regression models adjusting for demographic, military, pre-TBI screening health, and VHA covariates examined risk ratios of incident outcomes based on CTBIE completion and mTBI status. Main Measures: Incident substance use disorders (SUDs), alcohol use disorder (AUD), opioid use disorder (OUD), overdose, and homelessness documented in VHA administrative records, and mortality as documented in the National Death Index, 3 years post-TBI screen. VHA outpatient utilization was also examined. Results: Compared with the no CTBIE group, the mTBI+ group had 1.28 to 1.31 times the risk of incident SUD, AUD, and overdose, but 0.73 times the risk of death 3 years following TBI screening. The mTBI− group had 0.70 times the risk of OUD compared with the no CTBIE group within the same period. The no CTBIE group also had the lowest VHA utilization. Conclusions: There were mixed findings on risk of adverse events for the no CTBIE group relative to the mTBI+ and mTBI− groups. Future research is needed to explore the observed differences, including health conditions and healthcare utilization, documented outside VHA among veterans who screen positive for TBI. [ABSTRACT FROM AUTHOR]